{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import jqdatasdk\n",
    "import pylab\n",
    "from jqdatasdk import get_all_securities\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "from scipy.interpolate import interp1d\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "pylab.rcParams['figure.figsize'] = (15.0, 8.0) # 显示大小"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "auth success \n"
     ]
    }
   ],
   "source": [
    "with open(\"../data/auth_key.key\") as key:\n",
    "    auth_key = key.read().split(\",\")\n",
    "    jqdatasdk.auth(auth_key[0], auth_key[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "now_date=datetime.datetime.now().strftime(\"%Y-%m-%d\")\n",
    "try:\n",
    "    stock_code = pd.read_csv(\"../data/stock_code_%s.csv\" % now_date)\n",
    "except Exception as e:\n",
    "    print('Error')\n",
    "    stock_code = jqdatasdk.get_all_securities(['stock'])\n",
    "    stock_code['code']=stock_code.index\n",
    "    stock_code.index=[x for x in range(len(stock_code))]\n",
    "    stock_code.to_csv(\"../data/stock_code_%s.csv\" % now_date,index=False)\n",
    "    del stock_code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_month_exchange_time(date):\n",
    "    '''\n",
    "    获取上个月交易时间段\n",
    "    :param date:\n",
    "    :return: start_time end_time\n",
    "    '''\n",
    "    start_time = datetime.datetime.now() + datetime.timedelta(days=-365)\n",
    "    end_time = datetime.datetime.now()\n",
    "    return start_time.strftime(\"%Y-%m-%d\"), end_time.strftime(\"%Y-%m-%d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_date,end_date=get_month_exchange_time(now_date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_data(row):\n",
    "    start_date,end_date=get_month_exchange_time(now_date)\n",
    "    path=\"../data/stock_data/stock_%s_%s.csv\" % (row,now_date)\n",
    "    temp = jqdatasdk.get_price(row, start_date=start_date, end_date=end_date,\n",
    "                                           frequency='minute',\n",
    "                                           fields=None,\n",
    "                                           skip_paused=False, fq='pre')\n",
    "    temp.to_csv(path,index=False)\n",
    "    print(temp.tail(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "stock_code['code'].map(save_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "平安银行=jqdatasdk.get_price('000001.XSHE', start_date=start_date, end_date=end_date,\n",
    "                                           frequency='minute',\n",
    "                                           fields=None,\n",
    "                                           skip_paused=False, fq='pre')\n",
    "平安银行['date']=平安银行.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "平安银行['date']=pd.to_datetime(平安银行['date'],format = '%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "平安银行.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "平安银行.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "plt.figure()\n",
    "plt.title('close',fontsize=18)\n",
    "index=平安银行['date'].values\n",
    "close=平安银行['close'].values\n",
    "\n",
    "max_indx=np.argmax(close)#max value index\n",
    "min_indx=np.argmin(close)#min value index\n",
    "\n",
    "plt.plot(index,close,'r-',label=r'close',lw=2)\n",
    "\n",
    "dt = datetime.datetime.utcnow()\n",
    "time_now=pd.to_datetime(index[max_indx]).strftime(\"%Y-%m-%d\")\n",
    "\n",
    "plt.plot(index[max_indx],close[max_indx],'ks')\n",
    "show_max='['+str(time_now)+' '+str(close[max_indx])+']'\n",
    "plt.annotate(show_max,xytext=(index[max_indx],close[max_indx]),xy=(index[max_indx],close[max_indx]))\n",
    "print(index[min_indx],close[min_indx],show_max)\n",
    "plt.plot(index[min_indx],close[min_indx],'gs')\n",
    "plt.xlabel(\"value\")#x轴上的名字\n",
    "plt.ylabel(\"date\")#y轴上的名字\n",
    "plt.show()\n",
    "# plt.plot(x,y2,'r-.',label='n=2',lw=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "index=index.astype('datetime64[ns]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale=1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_index=np.linspace(min(index.astype('float')), max(index.astype('float')), scale).astype('datetime64[ns]').astype('datetime64[s]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.datetime64('2019-10-27').astype(datetime.datetime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0, 1, len(index))\n",
    "y = close\n",
    " \n",
    "# 分别用linear和quadratic插值\n",
    "fl = interp1d(x, y, kind='linear')\n",
    "fq = interp1d(x, y, kind='quadratic')\n",
    "fc = interp1d(x, y, kind='cubic')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "xint = np.linspace(x.min(), x.max(), scale)\n",
    "yintl = fl(xint)\n",
    "yintq = fq(xint)\n",
    "yintc = fc(xint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(new_index,yintl, color=\"r\", label = \"Linear\")\n",
    "plt.plot(new_index,yintq, color=\"b\", label =\"Quadratic\")\n",
    "plt.plot(new_index,yintc, color=\"y\", label =\"cubc\")\n",
    "plt.plot(index,close,'g-',label='close')\n",
    "# plt.plot(index,close,'gs')\n",
    "plt.legend(loc = \"best\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "close_fft=np.fft.fft(close)\n",
    "shifted = np.fft.fftshift(close_fft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_index=np.argmax(np.abs(close_fft))\n",
    "indexf=np.arange(len(np.abs(close_fft)/(len(index)/2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.grid()\n",
    "plt.plot(indexf,np.abs(close_fft)/(len(index)/2),'g-',label='close')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.linspace(-10*np.pi*0.001,10*np.pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "y=np.sin(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_fft=np.fft.fft(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_fftshift = np.fft.fftshift(y_fft)\n",
    "xf = np.arange(len(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22768567048>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.grid()\n",
    "min_index=np.argmax(np.abs(y_fft))\n",
    "print(xf[min_index])\n",
    "# print(np.abs(y_fftshift)[min_index])\n",
    "plt.subplot(121)\n",
    "plt.plot(xf,np.abs(y_fftshift)/(len(x)/2)  ,'r',label='close')\n",
    "plt.subplot(122)\n",
    "plt.plot(x,y,'r',label='origin')\n",
    "# plt.plot(xf,y,'g-',label='close')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\python36\\lib\\site-packages\\ipykernel_launcher.py:34: MatplotlibDeprecationWarning: Support for uppercase single-letter colors is deprecated since Matplotlib 3.1 and will be removed in 3.3; please use lowercase instead.\n",
      "d:\\python36\\lib\\site-packages\\ipykernel_launcher.py:38: MatplotlibDeprecationWarning: Support for uppercase single-letter colors is deprecated since Matplotlib 3.1 and will be removed in 3.3; please use lowercase instead.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "\n",
    "Fs = 150.0;                 # sampling rate采样率\n",
    "Ts = 1.0/Fs;                # sampling interval 采样区间\n",
    "t = np.arange(0,1,Ts)       # time vector,这里Ts也是步长\n",
    "\n",
    "ff = 25;                    # frequency of the signal\n",
    "y = np.sin(2*np.pi*ff*t)\n",
    "\n",
    "n = len(y)                  # length of the signal\n",
    "k = np.arange(n)\n",
    "T = n/Fs\n",
    "frq = k/T                   # two sides frequency range\n",
    "frq1 = frq[range(int(n/2))] # one side frequency range\n",
    "\n",
    "YY = np.fft.fft(y)          # 未归一化\n",
    "Y = np.fft.fft(y)/n         # fft computing and normalization 归一化\n",
    "Y1 = Y[range(int(n/2))]\n",
    "\n",
    "fig, ax = plt.subplots(4, 1)\n",
    "\n",
    "ax[0].plot(t,y)\n",
    "ax[0].set_xlabel('Time')\n",
    "ax[0].set_ylabel('Amplitude')\n",
    "\n",
    "ax[1].plot(frq,abs(YY),'r') # plotting the spectrum\n",
    "ax[1].set_xlabel('Freq (Hz)')\n",
    "ax[1].set_ylabel('|Y(freq)|')\n",
    "\n",
    "ax[2].plot(frq,abs(Y),'G')  # plotting the spectrum\n",
    "ax[2].set_xlabel('Freq (Hz)')\n",
    "ax[2].set_ylabel('|Y(freq)|')\n",
    "\n",
    "ax[3].plot(frq1,abs(Y1),'B') # plotting the spectrum\n",
    "ax[3].set_xlabel('Freq (Hz)')\n",
    "ax[3].set_ylabel('|Y(freq)|')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
